A time-varying grey Riccati model based on interval grey numbers for China's clean energy generation predicting. Issue 3 (15th July 2021)

• Record Type: Journal Article
• Authors: Guo, Sandang; Jing, Yaqian
• Is Part Of: Grey systems. Volume 12:Issue 3(2022)
• Page Start: 501
• Page End: 521
• Publication Date: 2021-07-15
• Subjects: Time-varying grey Riccati model -- Interval grey number sequences -- Clean energy generation; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.5
• DOI: 10.1108/GS-04-2021-0057
• Languages: English
• ISSNs: 2043-9377

Abstract

Abstract : Purpose: In order to accurately predict the uncertain and nonlinear characteristics of China's three clean energy generation, this paper presents a novel time-varying grey Riccati model (TGRM(1, 1)) based on interval grey number sequences. Design/methodology/approach: By combining grey Verhulst model and a special kind of Riccati equation and introducing a time-varying parameter and random disturbance term the authors advance a TGRM(1, 1) based on interval grey number sequences. Additionally, interval grey number sequences are converted into middle value sequences and trapezoid area sequences by using geometric characteristics. Then the predicted formula is obtained by using differential equation principle. Finally, the proposed model's predictive effect is evaluated by three numerical examples of China's clean energy generation. Findings: Based on the interval grey number sequences, the TGRM(1, 1) is applied to predict the development trend of China's wind power generation, China's hydropower generation and China's nuclear power generation, respectively, to verify the effectiveness of the novel model. The results show that the proposed model has better simulated and predicted performance than compared models. Practical implications: Due to the uncertain information and continuous changing of clean energy generation in the past decade, interval grey number sequences are introduced to characterize full information of the annual clean energy generation data. And the novel TGRM(1, 1) is applied to predict upper and lower bound values of China's clean energy generation, which is significant to give directions for energy policy improvements and modifications. Originality/value: The main contribution of this paper is to propose a novel TGRM(1, 1) based on interval grey number sequences, which considers the changes of parameters over time by introducing a time-varying parameter and random disturbance term. In addition, the model introduces the Riccati equation into classic Verhulst, which has higher practicability and prediction accuracy.